Wang et al. Military Med Res (2021) 8:45 https://doi.org/10.1186/s40779-021-00336-1
REVIEW
Advances in single-cell sequencing: insights from organ transplantationYing Wang1†, Jian‑Ye Wang2†, Angelika Schnieke1 and Konrad Fischer1*
Abstract
Single‑cell RNA sequencing (scRNA‑seq) is a comprehensive technical tool to analyze intracellular and intercellular interaction data by whole transcriptional profile analysis. Here, we describe the application in biomedical research, focusing on the immune system during organ transplantation and rejection. Unlike conventional transcriptome analy‑sis, this method provides a full map of multiple cell populations in one specific tissue and presents a dynamic and transient unbiased method to explore the progression of allograft dysfunction, starting from the stress response to final graft failure. This promising sequencing technology remarkably improves individualized organ rejection treat‑ment by identifying decisive cellular subgroups and cell‑specific interactions.
Keywords: Single‑cell RNA sequencing, Transplant rejection, Immune cell interactions, Transcriptional profiling, 10× Genomics chromium
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BackgroundThe demand for organs due to end-stage organ failure is permanently increasing. The best solution is autolo-gous transplantation, enabled by patient-derived induced pluripotent stem cells. This approach is currently envis-aged for single cells and basic cellular units such as islets. Tissue engineering is being optimized as one solution for larger organs. However, this option is not realistic pres-ently. Xenotransplantation could soon become available clinically and an alternative backup for allotransplanta-tion. However, both will depend on matching grafts and require immune suppression. The importance of under-standing immune reactions, identifying cellular subtypes involved in graft acceptance and tolerance induction and identifying early indicators for rejection mechanisms require detailed immune cell profiling.
The immune system is a complex biological network comprising multiple layers of orchestrated genes, pro-teins and cells. In response to the challenge of pathogens or transplants, the immune system triggers various inter-actions between immune cells and other cells, provok-ing specific responses [1]. Innate and adaptive immune cells interact to ensure tissue protection according to functional requirements. Disruption of immune system homeostasis causes transplant rejection [2, 3]. Although inhibition of acute rejection has improved significantly in the past two decades, long-term rejection and immuno-suppression can lead to high morbidity and mortality, and chronic transplant rejection can cause irreversible dam-age to the graft [4, 5]. The most common clinical acute rejection is mainly mediated by cellular responses, while hyperacute rejection and chronic rejection are mainly mediated by humoral immunity. The transplant rejection mechanism is an immunological reaction that recognizes foreign molecules of the donor cells, triggering attack and destruction. Various immunological factors are involved in acute and chronic rejection, including human leuko-cyte antigen (HLA) mismatch, donor-specific antibodies and non-immune factors (e.g., donor age, infection, and immunosuppressive drug toxicity) [6, 7].
Open Access
*Correspondence: [email protected]†Ying Wang and Jian‑Ye Wang have contributed equally to this review.1 Chair of Livestock Biotechnology, School of Life Sciences Weihenstephan, Technical University of Munich, Liesel Beckman Str 1, 85354 Freising, GermanyFull list of author information is available at the end of the article
Page 2 of 11Wang et al. Military Med Res (2021) 8:45
Assessing the immune reactionCellular and molecular assays to measure immune cell differentiation, cellular function and antigen specificity contribute to solving important problems in immune-mediated transplant rejection. Fluorescence-activated cell sorting (FACS) is used for post-transplantation immune surveillance to identify cells of the immune sys-tem by detecting surface marker expression or intracel-lular proteins, including cytokines. This tool has been supplemented by in situ histological tests [8, 9]. However, histological diagnosis may miss subtle alterations in indi-vidual patients, which are essential for rejective pathol-ogy [10]. Therefore, the combination of transcript sets and histological diagnosis of tissue samples is used to predict antibody-mediated or cellular rejections [11, 12]. Additionally, bulk RNA sequencing of allograft biopsies is a method to determine gene-specific single-nucleotide variants of donors and recipients [13]. Microarray tech-nology has been used to study the pathogenesis of trans-plant rejection processes. For example, Roedder et al. [14] used microarray technology to determine a test set of 17 relevant genes to predict renal rejection. Each of these approaches provides valuable insights. However, they are subject to limitations due to the complex immune rejection response. Furthermore, extensive analysis can-not resolve phenotypic heterogeneity and distinguish the gene profile of donors and recipients in mixed cell popu-lations [15]. Even with the application of Cytometry by Time of Flight (CyTOF), it remains challenging to assess a truly unbiased dataset of a single cell, a process that requires single-cell resolution [16]. Among single-cell profiling methods, scRNA-seq comprehensively meas-ures the transcriptional expression of bulk cells [1, 17] and quantitatively analyzes all transcripts expressed in a single cell, providing an unbiased strategy to identify and characterize different cell populations [18–20].
This review discusses the development and application of scRNA-seq in organ transplantation. This cutting-edge technology will improve immunotherapies and help to predict recipient outcomes.
Sample harvest and tissue processingThe entire process starting with sample isolation to the final evaluation of scRNA-seq data is summarized in Fig. 1. The first step of scRNA-seq analysis is the dis-sociation of the graft tissue, which is obtained in most cases by biopsies. The currently used protocols were developed and improved over decades, and each has its strengths and weaknesses. For transplanted organs, laser capture microdissection, digestion or enzyme-related approaches, followed by density gradient centrifuga-tion or fluorescence-activated cell sorting are used. Tis-sue dissociation is more difficult to implement for frozen
samples, e.g., those of the liver [21]. In particular, for liver samples, a different sensitivity to cell death caused by the dissociation step may result in bias because hepatocytes die very fast, while other cells become activated during tissue dissociation, indicating a transcriptional stress response. Macparland et al. [22] developed a milder approach to reduce the cell damage rates by maintaining the tissue at 4 °C during all steps, including collagenase perfusion. Wang et al. [23] used a hypothermic strategy for kidney preservation for up to 4 days for scRNA-seq analysis. Recently, Guillaumet-Adkins et al. [24] pub-lished an improved method for sample preservation, gradual freezing by 1 ℃ per minute to -80 ℃, that does not change the transcriptional profiles and makes cryo-preserved cells and tissues applicable for scRNA-seq. However, frozen tissues cannot be processed like fresh tissues. Tissue dissociation leads to the loss of spatial and anatomical information for cells, and the precise location of each cell should not be ignored. To address this issue, RNA probes identifying cellular transcriptional organiza-tion [25] or spatial transcriptomic protocols [26] are both helpful alternative methods.
Single‑cell methodsThe next steps after tissue dissociation include RNA cap-ture, reverse transcription, RNA sequencing, and library construction. Selection of a suitable sequencing method is challenging because several methods exist, such as CEL-seq2, Drop-seq, MARS-seq, MATQ-seq, Quartz-seq, SCRB-seq, Smart-seq, Smart-seq2, Drop-seq, FB5P-seq, SPLIT-seq, and DNBelab C4 [27–30]. We summarize the most commonly used scRNA-seq methods in Table 1 according to their capturing format, cDNA amplifica-tion, sequencing method, transcript coverage advantages and limitations. Microfluidic technologies for scRNA-seq involve droplet-based and plate-based technolo-gies. Amplification is performed by PCR for Smart-seq [31, 32] and Quartz-seq [33, 34] and in vitro transcrip-tion, generating RNA in vitro, e.g., by InDrop [35] and CEL-seq [36, 37]. Drop-seq [38], InDrop [35], and CEL-seq [36, 37] incorporate unique molecular identifiers (UMIs) into cDNA. A UMI is a short sequence barcode to detect and quantify transcripts. These molecular bar-codes uniquely tag each molecule in a sample library and reduce quantitative and error biases introduced by amplification. Smart-seq2, Quartz-seq, and MATQ-seq produce almost full-length sequencing data, while oth-ers (e.g., CEL-seq and Drop-seq) only capture the 3’-end sequence or 5’-sequence (e.g., FB5P-seq and STRT-seq) [10, 30, 39, 40]. Each platform provides multiple and specific but not completely comprehensive advantages in data capture. The reduction of mRNA amplification noise by CEL-seq2, InDrop, Drop-seq, MARS-seq, and
Page 3 of 11Wang et al. Military Med Res (2021) 8:45
SCRB-seq is a favorable feature that makes these plat-forms preferable. However, MARS-seq, SCRB-seq, and particularly Smart-seq2 platforms capture more genes using the same number of cells, making them prefer-able for relatively low quantities [41]. Drop-seq analyzes thousands of individual cells simultaneously without losing the original transcript [38]. Compared with other widely used single-cell RNA sequencing platforms (such as Smart-seq2), 10× Genomics Chromium is a more cost-effective and time-efficient system. This platform generates droplets and forms a single-cell suspension. Additionally, it can process many cells and detect even rare cell types or transcripts [42] by combining one of the following methods: InDrop for rare cell populations [38] or CEL-seq for complex tissues containing multiple cell populations [36, 37]. Smart-seq increases the ther-mal stability of DNA base pairs [31, 32]. MATQ-seq is implemented on low-abundance genes and noncoding and non-polyadenylated RNA [43]. Quartz-seq is applied
to detect the different cell cycle phases and transcrip-tome heterogeneity. SCRB-seq is used for heterogeneous populations [33, 34]. FB5P-seq [30] and T-cell recep-tor repertoire sequencing (TCR-seq) [44] are used to identify the repertoire and diversity of BCRs and TCRs. STRT-seq tracks the cell origin efficiency without quan-titative bias against long transcripts. One of the common disadvantages is the limited throughput and read cover-age, e.g., by Smart-seq 1 and 2 [31, 32]. Another is the requirement for skilled technicians, e.g., for Quartz-seq 1 and 2 [33, 34]. Because each method has weaknesses (Table 1), investigators must choose a platform according to their specific interests.
Data analysisData analysis after sequencing generally comprises qual-ity control (QC), batch effect correction, normalization, data imputation, dimensionality adjustment, subsequent expression analysis and cell subpopulation identification
Fig. 1 Steps of scRNA‑seq to analyzing organ rejection. After biopsies of graft tissues, cells are isolated and can be used for droplet‑ or plate‑based sequencing approaches. After batch effect correction, normalization, data imputation and dimensionality adjustments, specific cellular subtypes can be identified. Analysis of heterogeneity and prediction of tolerance is used to identify target genes and molecular interactions that are the basis for gene therapy approaches and long‑term graft acceptance. QC quality control; DCs dendritic cells
Page 4 of 11Wang et al. Military Med Res (2021) 8:45
Tabl
e 1
Com
paris
on o
f scR
NA
met
hods
IVT
In v
itro
tran
scrip
tion,
UM
I Uni
que
mol
ecul
ar id
entifi
ers
Met
hod
Capt
ure
form
atcD
NA
am
plifi
catio
nTr
ansc
ript
cov
erag
eM
olec
ule
iden
tifier
Stre
ngth
Wea
knes
sRe
fere
nces
Dro
p‑se
qD
ropl
ets
PCR
3′ e
ndU
MI
For i
ndiv
idua
l cel
ls; s
imul
tane
ous
anal
ysis
with
out l
osin
g th
e or
igin
al
tran
scrip
t; hi
gh th
roug
hput
, low
er
cost
s an
d re
duct
ion
of th
e m
RNA
am
plifi
catio
n no
ise
Onl
y 3′
end
is e
nabl
ed fo
r spe
cific
the
ampl
ifica
tion
[38]
10×
Gen
omic
s C
hrom
ium
Dro
plet
sPC
R3′
end
UM
IIn
crea
sed
thro
ughp
ut, c
ost e
ffect
ive
and
time
effici
ent
Onl
y 3′
end
is e
nabl
ed fo
r spe
cific
am
plifi
catio
n, e
ffici
ency
loss
es[4
5]
InD
rop
Dro
plet
sIV
T3′
end
UM
ID
etec
ts ra
re c
ell p
opul
atio
ns; h
igh
thro
ughp
ut, l
ow n
oise
pro
file
Low
cel
l cap
ture
effi
cien
cy[3
5]
CEL
‑seq
1 &
2Pl
ate
IVT
3′ e
ndU
MI
For c
ompl
ex ti
ssue
s co
ntai
ning
m
ultip
le c
ell p
opul
atio
ns; i
mpr
oved
ac
cura
cy a
nd h
ighe
r sen
sitiv
ity;
redu
ces
mRN
A a
mpl
ifica
tion
nois
e
Low
cel
l cap
ture
effi
cien
cy[3
6, 3
7]
Smar
t‑se
q 1
& 2
Plat
ePC
RFu
ll le
ngth
UM
IIn
crea
ses
ther
mal
sta
bilit
y of
DN
A
base
pai
rsLo
wer
det
ectio
n effi
cien
cy, l
imite
d th
roug
hput
and
read
cov
erag
e[3
1, 3
2]
MAT
Q‑s
eqPl
ate
PCR
Full
leng
thN
AFo
r low
‑abu
ndan
ce g
enes
and
non
‑co
ding
and
non
‑pol
yade
nyla
ted
RNA
; hig
hly
sens
itive
with
qua
ntita
‑tiv
e de
tect
ion
effici
ency
Low
thro
ughp
ut[4
3]
Qua
rtz‑
seq
1 &
2Pl
ate
PCR
Full
leng
thN
A (Q
uart
z‑se
q),
UM
I (Q
uart
z‑se
q 2)
For d
iffer
ent c
ell c
ycle
pha
ses
and
tran
scrip
tom
e he
tero
gene
ity
dete
ctio
n; h
igh
quan
tity
and
effi‑
cien
cy in
lim
ited
sequ
ence
read
s
Requ
ires
skill
ed te
chni
cian
[33,
34]
SCRB
‑seq
Plat
ePC
R3′
end
UM
IFo
r het
erog
eneo
us p
opul
atio
n id
en‑
tifica
tion;
hig
h th
roug
hput
, low
co
st, s
impl
e st
eps,
few
er p
oten
tial
bias
es, r
educ
ed m
RNA
am
plifi
ca‑
tion
nois
e
Requ
ires
skill
ed te
chni
cian
[46]
FB5P
‑seq
Plat
ePC
R5′
end
UM
IFo
r BC
R an
d TC
R re
pert
oire
iden
tifica
‑tio
n; c
ost a
nd ti
me
effec
tive;
inte
‑gr
ativ
e an
alys
is o
f tra
nscr
ipto
me
3′ e
nd s
cRN
A‑s
eq p
roto
cols
are
not
su
itabl
e[3
0]
STRT
‑seq
Plat
ePC
R5′
end
UM
IIm
prov
es e
ffici
ency
and
trac
king
of
cel
l orig
in; n
o qu
antit
ativ
e bi
as
agai
nst l
ong
tran
scrip
ts
Tech
nica
l var
iatio
n[4
7]
TCR‑
seq
Plat
ePC
R3′
end
NA
For T
‑cel
l div
ersi
ty id
entifi
catio
nN
o st
anda
rdiz
ed th
resh
olds
, dis
pari‑
ties
betw
een
diffe
rent
stu
dies
[44]
MA
RS‑s
eq 1
& 2
Plat
eIV
T3′
end
UM
IFo
r in
vivo
tran
scrip
tiona
l sta
tes
in
thou
sand
s of
sin
gle
cells
; min
imiz
es
ampl
ifica
tion
bias
Requ
ires
skill
ed te
chni
cian
[48]
Page 5 of 11Wang et al. Military Med Res (2021) 8:45
[39]. QC is required for high technical noise and low-quality data, which are often generated because of dead cells. Batch effects are caused by large-scale scRNA-seq datasets, samples prepared in different laboratories, even those based on the same protocol, and large data gener-ated from separate technicians or different time points [49, 50]. They can cause systematic errors and differ-ences among multiple transcriptional profiles. Thus, batch effect correction is vital to avoid this misinterpreta-tion, and removing unwanted variation (RUV) is a good normalization strategy to adjust confounding technical effects [51]. Data normalization is necessary to adjust technical biases. Two types of normalization are used: sample normalization, which adjusts within-sample dif-ferences, and gene profile normalization, which elimi-nates gene expression biases. Data imputation is an effective strategy to insert substituted values into drop-outs, eliminating the influence of missing data. Because of technological developments in scRNA-seq and bulk data generation with high dimensionalities, compu-tational bioinformatics analysis is essential to process raw data. Automatic annotation methods such as the "SingleR" package [52], online databases [53] and gene expression markers [54] can be used for cell marker iden-tification. The commonly used "SingleR" labels new cells from the scRNA-seq dataset based on similarity to the reference dataset of samples with known labels.
Challenges during data analysis involve multiple biases in the entire procedure and high dimensional datasets [55]. The possibility of low-quality data or dropouts in the scRNA-seq results, caused by low viability or dead cells, can lead to misinterpretation [56]. An optimal method can avoid false results to enable correct transcrip-tional interpretation (Table 2). "Seurat" is an R package designed for QC, analysis and exploration of scRNA-seq data to reduce some biases [54, 57]. To distinguish tech-nical biases from biological signals, Bayesian Inference for Single-cell ClUstering and ImpuTing (BISCUIT) pro-vides a discernible advantage for graphical algorithms. BISCUIT imputes dropouts and improves both cluster-ing and normalization [58]. Using these tools, the data are clustered to reduce technical variations (amplification bias, sequencing depth, GC content, capture inefficiency,
and RNA content variations [59]). Because scRNA-seq data contain many genes and cells in high dimensions, large-scale computational resources are required. T-dis-tributed stochastic neighbor embedding (tSNE) is an approach for high-dimensional data because of the com-putation time and potentially manifold embeddings for the same datasets from run to run [60]. PhenoGraph is also an algorithmic method used for partitions of high-dimensional data into distinct subgroups within complex tissues [61]. Dimensional reduction and uniform mani-fold approximation and projection (UMAP) are alterna-tive methods for data analysis developed to project the data into lower dimensions and visualize cell clusters with high reproducibility without losing the properties of the original data [62]. Xu et al. described a new clus-tering algorithm of graph-based shared nearest neighbor (SNN)-Cliq implemented in Python and MATLAB soft-ware, considering the surrounding data points, including low-density region data and detecting more cell subtypes with high accuracy [63]. For zero-inflated data compris-ing an excessive number of zeros, zero inflated factor analysis (ZIFA) is a novel approach to reduce dataset dimensions [64]. "Harmony" is another R package with an efficient batch algorithm method to integrate multi-ple datasets and requires fewer computational resources. Korsunsky et al. [65] developed this method (https:// github. com/ immun ogeno mics/ harmo ny) and applied it to large datasets integrated with spatial transcriptomics data.
However, these algorithms are most commonly used for static analysis. Another promising means, consid-ering a continuous transition between different cellu-lar states, are the machine learning approaches listed in Table 3, Monocle [66], Monocle2 [67], TSCAN [68], Wishbone [69], Slingshot [70], Diffusion pseudotime software [71] and Wanderlust [72] allow the construc-tion of trajectories of cells in dynamic gene regulation and explain normal physiological and pathophysiologic alterations of cellular subgroups in specific locations of the human body. Saelens et al. [73] concluded that Sling-shot, TSCAN and Monocle2 exhibited better trajectory identification. Therefore, the combination of personal-ized medicine and artificial intelligence will become
Table 2 Tools for scRNA‑seq data analysis
Challenge Method References
Multiple biases "Seurat" is an R package designed for quality control, analysis, and exploration of scRNA‑seq data [54, 57]
BISCUIT provides the graphical algorithm and imputes dropouts, improving both clustering and normalization and reducing the technical biases from biological signals
[58]
Dimension tSNE, PhenoGraph and ZIFA are used for high dimensional data. UMAP and SNN project the data into lower dimensions [59–64]
"Harmony" is an efficient batch algorithm method to integrate multiple datasets and requires fewer computational resources
[65]
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applicable in the near future [41]. This combination will provide a map of cells, considering temporal dynamics and spatial positioning by evaluating the pathological microenvironment, phase of the cell cycle and responses to clinical medication. For transplanted organs, selecting an optimized process for accurate subsequent analysis is highly recommended.
Applications of scRNA‑seq for transplantationCurrently, the main difficulties for successful transplanta-tion comprise, among others, best-matched donor selec-tion and a reduction in lifelong immunosuppression [74]. For transplant rejection, the latest advances in scRNA-seq provide an opportunity to fully reveal new cell types and states without result bias and RNA degradation [75]. Snapshots of the single-cell transcriptome exhibit various stages of immune differentiation and activation, while these stages are rarely synchronized among cells [76]. At single-cell resolution, it can describe immune cells, stro-mal cells and new cell subtypes that undergo immune rejection and further compare the unique characteristics of the signaling pathways between different cellular sub-groups [57]. Here, we describe the advantages of scRNA-seq in the fields of kidney, liver, lung and hematopoietic stem cell (HSC) transplantation and for immunological applications.
Kidney transplantationT cells play a crucial role in graft rejection. Most stud-ies have focused on bulk methods based on biopsy samples without providing information about αβ chain pairing of T-cell receptors (TCRs) [77]. This lack can underestimate actual library differences and fails to reflect that T cells with the same TCR can exert opposite biological functions. ScRNA-seq technol-ogy overcomes the abovementioned limitations and brings library analysis a higher diversity [78]. ScRNA-seq detects T-cell subclones [79]. For example, Morris et al. [80] monitored donor-reactive T cells in patients with tolerant and non-tolerant kidney transplantation.
Donor reactivity has been detected in patients with tol-erance. The decrease in T-cell clones in non-tolerant patients did not show a reduced number of donor-reactive T cells. Contrary to data from tolerant patients undergoing standard immunosuppression, an expan-sion of donor-reactive T-cell clones was observed in peripheral blood [81–83]. Antibody-mediated rejection (AMR) in the kidney is not easily identified by histo-logical diagnoses. However, a transcriptional expres-sion profile can strengthen the diagnosis. AMR injury is the most common driver of late allograft loss [12, 84]. Several groups have performed scRNA-seq of kidney allograft undergoing AMR, where monocytes, B cells, plasma cells and T cells invade into the tissue [85] and donor endothelial cells (ECs) are the primary targets of the recipients’ humoral immune response [86, 87]. Thus, scRNA-seq provides a comprehensive under-standing of subtle mechanisms in conceptualizing het-erogeneous kidney rejection. Macrophages and T cells, activated in the recipient, significantly differ from the original populations in either the donor or recipient. Some of these immune cells exist for only a few days after transplantation, but macrophages can persist for several years [88]. Furthermore, immune cell popula-tions residing in donor-derived tissues can be replaced by recipient cells, particularly during rejection [89]. Liu et al. [57] presented a novel heterogeneous pro-file of immune cells based on allograft biopsies and matching healthy kidneys by scRNA-seq, integrating the key alterations of molecular functions, establishing therapeutic surveillance for kidney allograft rejection and improving allograft survival [90]. They identified subclusters of cytotoxic T lymphocytes that exhibit a more proinflammatory role in renal allograft rejec-tion, while activated B cells interacted with surround-ing stromal cells, mostly emerging in allograft kidneys, leading to immune cell recruitment and an activated inflammatory response. Non-invasive urine or blood biomarkers are applicable for a limited group of pathol-ogies [91], whereas invasive biopsies are used to profile
Table 3 Tools for scRNA‑seq trajectory inference
Method URL References
Monocle http:// cole‑ trapn ell‑ lab. github. io/ monoc le‑ relea se/ [66]
Monocle2 http:// cole‑ trapn ell‑ lab. github. io/ monoc le‑ relea se/ [67]
TSCAN https:// github. com/ zji90/ TSCAN [68]
Wishbone https:// github. com/ ManuS etty/ wishb one [69]
Slingshot https:// github. com/ kstre et13/ sling shot [70]
Diffusion Pseudotime Software https:// static‑ conte nt. sprin ger. com/ esm/ art% 3A10. 1038% 2Fnme th. 3971/ Media Objec ts/ 41592_ 2016_ BFnme th3971_ MOESM 375_ ESM. zip
[71]
Wanderlust https:// github. com/ wande rlust/ wande rlust [72]
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non-circulating immune cells in transplant rejection, providing more diagnostic accuracy and prognostic biomarkers amenable to therapeutic tools [10, 85].
Liver transplantationThe interaction of immune cells and liver cells in a trans-plant setting is a key mechanism for liver tolerance induc-tion [92]. ScRNA-seq can improve the hepatic immune cellular map in interpreting the specific CD4+ T-cell subgroup from other T cells in liver transplant rejection and tolerance [93]. Immune cells such as dendritic cells (DCs), T cells, and probably NK cells interact with liver sinusoidal ECs and hepatocytes, adding specific signal-ing molecules to generate a tolerogenic state. Apoptosis of infiltrating T cells may be critical for allograft toler-ance [93, 94]. Despite possible tolerance induction after liver transplantation, human liver allografts are likely to be rejected without applying immunosuppressive drugs [95]. Approximately 10%-30% of allograft recipients are diagnosed with acute cellular rejection [96], likely due to T-cell-related immune responses [97]. Applying scRNA-seq may better elucidate the cellular immune responses in the liver allograft.
Lung transplantationUsing scRNA-seq to test single cells isolated from lung transplantation donors and lung fibrosis has revealed transcriptionally distinct populations of alveolar mac-rophages that express profibrotic genes in patients with pulmonary fibrosis [98, 99]. Some mesenchymal cell markers that play a role in Wnt/β-catenin signaling dur-ing lung regeneration and some previously described rare cell populations have been identified. These technologies can improve the diagnosis of patients with fibrosis after lung transplantation and can be used to identify patients most likely to benefit from targeted therapy and monitor their response during disease progression [100]. Mould et al. [101] assessed the cell populations between healthy samples and found highly conserved cellular heterogene-ity in bronchoalveolar lavage (BAL) cells. By dynamically comparing the lungs of donors and recipients, persistent donor resident memory T cells are correlated with better clinical outcomes [102], providing a potential therapeutic tool for extended allograft survival.
HSC transplantationTo study the rejection of transplanted HSCs, Dong et al. [103] used scRNA-seq to obtain a transcriptome-based classification of 28 hematopoietic cell types. According to this classification, most transplanted HSCs are dedicated to transcriptional immunophenotypical multipotent pro-genitors (tMPP1). Transcriptional analysis and functional evaluation showed that the proliferation of transplanted
cells is accompanied by a gradually decreased percentage of HSCs [104]. However, a balance between proliferation, differentiation and stem cell maintenance likely exists. Graft-versus-host disease (GVHD) is the main compli-cation of allogeneic hematopoietic cell transplantation (HCT) [105]. Acute GVHD is mediated by donor T cells [106]. TCR-seq, as a type of scRNA-seq, can clarify how acute GVHD occurs. Although donor T-cell pools have highly similar TCRs, the TCR repertoire after HCT is very specific to recipients. TCR recombination is highly stochastic and may not depend on evaluating the most expanded TCR clones in any single recipient but on the complex polyclonal architecture. These results can be used to guide clinical decisions to prevent or treat acute GVHD [78]. By analyzing skin and peripheral blood T cells using TCR-seq, host skin-resident T cells were found to have an unanticipatedly pathogenic impact on GVHD [107].
Immunological applications of scRNA‑seqScRNA-seq identifies gene profiles of various cell popu-lations. This technical tool avoids the weakness of bulk analysis, which is likely to miss cell-specific signatures [85]. It also improves the understanding of immune cell ontogeny and interaction with stromal cells in a given organ. The advantages of scRNA are maximized by its combination with databases or improvements by other techniques [such as single-nucleus RNA sequencing (snRNA-seq)] [108]. The Immune Cell Atlas (ICA) is an essential part of the international Human Cell Atlas ini-tiative (https:// www. human cella tlas. org/), which collects samples from patients who have undergone rejection response and evaluates different reaction stages using scRNA-seq technology. By visualizing the dynamic observation of cell processes, the subtle transcriptional differences among cell types can be qualitative. The rejec-tion response, caused by specific gene regulation, pro-vides robust target genes and molecular mechanisms to diagnose and treat transplant rejection and identify potential diagnostic markers [109, 110]. The limitations of scRNA-seq due to fresh tissue requirements, arti-factual transcriptional biases and loss of fragile or low viability cells can be overcome by snRNA-seq, enabling the storage of frozen samples and analysis using a qual-ity comparable to scRNA-seq [108, 111–113]. The lat-est developments in snRNA-seq, imaging technologies [such as single-molecule fluorescence in situ hybridiza-tion (FISH)], proteomics (MIBI-TOF), and genomics all together provide benefits to further investigate cellular functions [114].
Classical immune cell characterization in differ-ent organs has limitations due to technologies such as
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microscopy and high-affinity antibody labeling. Addi-tionally, conventional transcriptome studies may miss some essential immune cell subtypes [85]. ScRNA-seq is currently widely used in immunology to unravel the differentiation process of HSCs, resolve previ-ously under recognized immune cellular heterogeneity, decipher the immune cellular repertoire and predict disease-related phenotypes [39, 115, 116]. ScRNA-seq can help identify HSC fate branch points during dif-ferentiation; for example, conventional dendritic cells (cDCs) rely on the abundance of Siglec-H and Ly6C to determine the direction of cDC type 1 (cDC1) or cDC type 2 (cDC2) [117]. The myeloid progenitor cells that produce mast cells and eosinophils or monocytes and macrophages depend on the presence of GATA1 [118]. Jaitin et al. [119] performed scRNA-seq on DCs and found different states of bone marrow-derived and other immune tissue-derived cells. Using scRNA-seq, progenitor immune cells can be identified by analyzing transcriptional variations in immature bone marrow and mature resident immune cells of specific organs [22]. Many new types of immune cells were identified using scRNA-seq, which can analyze cell differentiation and cell lineages, including innate lymphocytes and lung interstitial macrophages [57]. Recent studies have shown the heterogeneity of hematopoietic progenitor cells with a mixed lymphoid phenotype using scRNA-seq. This technology can also be used to identify novel cell types in several diseases, for example, cancer diag-nosis and efficacy evaluation. By analyzing circulating tumor cells (CTCs) from prostate cancer patients, Miy-amoto et al. [120] found that CTCs are highly heteroge-neous in gene expression. The special feature of CTCs is that they activate Wnt signaling, which increases resistance to drug therapy. By analyzing brain cells in Alzheimer’s disease, scRNA-seq also enables the iden-tification of microglia and perivascular macrophages related to neurodegenerative diseases [121]. ScRNA-seq also improves the diagnosis of disease heterogene-ity, such as identifying specific B-cell receptor signaling pathways and gene expression patterns in non-Hodg-kin’s lymphoma. Most adult B-cell lymphomas exhibit a B-cell phenotype at the germinal center (GC). By com-bining scRNA-seq, the mixed characteristics of B cells derived from GC and follicular lymphoma (FL) revealed unique transcription characteristics [122, 123]. In the future, organ transplantation single-cell sequenc-ing will likely help further elucidate the pathogenesis of transplant rejection and initiate the development of clinical trials and the emergence of more effective drugs to reduce the immune response associated with
transplant rejection, thereby improving the quality of life of patients and extending patient survival.
ConclusionTaken together, scRNA-seq can accurately interpret gene expression data, recognize cell heterogeneity, including new cell types or subtypes, and take snapshots of gene expression during the transition of cells from one state to another. All data can be integrated to define the critical process of cell development and differentiation, reveal the key signaling pathways and understand the gene regulatory network that predicts immune function [124–127].
AbbreviationsAMR: Antibody‑mediated rejection; BISCUIT: Bayesian Inference for Single‑cell ClUstering and ImpuTing; cDC: Conventional dendritic cell; CTC : Circulating tumor cell; CyTOF: Cytometry by Time of Flight; ECs: Endothelial cells; FACS: Fluorescence Activated Cell Sorting; FL: Follicular lymphoma; GC: Germinal center; GVHD: Graft‑versus‑host disease; HCT: Hematopoietic cell transplanta‑tion; HLA: Human leukocyte antigen; HSCs: Hematopoietic stem cells; ICA: Immune Cell Atlas; IVT: In vitro transcription; QC: Quality control; RUV: Remove unwanted variation; scRNA‑seq: Single‑cell RNA sequencing; SNN: Shared nearest neighbor; SnRNA‑seq: Single‑nucleus RNA sequencing; TCR‑seq: T‑cell receptor repertoire sequencing; tMPP1: Transcriptional immunophenotypical multipotent progenitors; tSNE: T‑distributed stochastic neighbor embedding; UMAP: Uniform manifold approximation and projection; UMI: Unique molecu‑lar identifiers; ZIFA: Zero inflated factor analysis.
AcknowledgementsThe authors thank Seq Health Technology Co., Ltd. (Wuhan, China) for provid‑ing guidance on sequencing technologies.
Authors’ contributionsYW and JYW drafted the manuscript. KF and AS reviewed and supervised the manuscript. All the authors read and approved the final manuscript.
FundingNot applicable.
Availability of data and materialsNot applicable.
Declarations
Ethics approval and consent to participateNot applicable.
Consent for publicationNot applicable.
Competing interestsThe authors declare that they have no competing interests.
Author details1 Chair of Livestock Biotechnology, School of Life Sciences Weihenstephan, Technical University of Munich, Liesel Beckman Str 1, 85354 Freising, Germany. 2 Department of Surgery, School of Medicine, Technical University of Munich, 81675 Munich, Germany.
Received: 30 October 2020 Accepted: 31 July 2021
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References 1. Hao S, Yan KK, Ding L, Qian C, Chi H, Yu J. Network approaches for dis‑
secting the immune system. iScience. 2020;23(8):101354. 2. Stubbington MJT, Rozenblatt‑Rosen O, Regev A, Teichmann SA. Single‑
cell transcriptomics to explore the immune system in health and disease. Science. 2017;358(6359):58–63.
3. Ingulli E. Mechanism of cellular rejection in transplantation. Pediatr Nephrol. 2010;25(1):61–74.
4. Menon MC, Keung KL, Murphy B, O’connell PJ. The use of genomics and pathway analysis in our understanding and prediction of clinical renal transplant injury. Transplantation. 2016;100(7):1405–14.
5. Claeys E, Vermeire K. Immunosuppressive drugs in organ transplanta‑tion to prevent allograft rejection: mode of action and side effects. J Immunol Sci. 2019;3(4):14–21.
6. Nankivell BJ, Borrows RJ, Fung CLS, O’connell PJ, Allen RDM, Chapman JR. The natural history of chronic allograft nephropathy. N Engl J Med. 2003;349(24):2326–33.
7. El‑Zoghby ZM, Stegall MD, Lager DJ, Kremers WK, Amer H, Gloor JM, et al. Identifying specific causes of kidney allograft loss. Am J Transplant. 2009;9(3):527–35.
8. Maguire O, Tario JD Jr, Shanahan TC, Wallace PK, Minderman H. Flow cytometry and solid organ transplantation: a perfect match. Immunol Invest. 2014;43(8):756–74.
9. Colvin RB. Antibody‑mediated renal allograft rejection: diagnosis and pathogenesis. J Am Soc Nephrol. 2007;18(4):1046–56.
10. Malone AF, Wu H, Humphreys BD. Bringing renal biopsy interpretation into the molecular age with single‑cell RNA sequencing. Semin Neph‑rol. 2018;38(1):31–9.
11. Sellares J, Reeve J, Loupy A, Mengel M, Sis B, Skene A, et al. Molecular diagnosis of antibody‑mediated rejection in human kidney transplants. Am J Transplant. 2013;13(4):971–83.
12. Halloran PF, Pereira AB, Chang J, Matas A, Picton M, De Freitas D, et al. Microarray diagnosis of antibody‑mediated rejection in kidney trans‑plant biopsies: an international prospective study (INTERCOM). Am J Transplant. 2013;13(11):2865–74.
13. Thareja G, Yang H, Hayat S, Mueller FB, Lee JR, Lubetzky M, et al. Single nucleotide variant counts computed from RNA sequencing and cellular traffic into human kidney allografts. Am J Transplant. 2018;18(10):2429–42.
14. Roedder S, Sigdel T, Salomonis N, Hsieh S, Dai H, Bestard O, et al. The kSORT assay to detect renal transplant patients at high risk for acute rejection: results of the multicenter AART study. PLoS Med. 2014;11(11):e1001759.
15. Higdon LE, Schaffert S, Khatri P, Maltzman JS. Single cell immune profil‑ing in transplantation research. Am J Transplant. 2019;19(5):1278–87.
16. Roy AL. Transcriptional regulation in the immune system: one cell at a time. Front Immunol. 2019;10:1355.
17. Giladi A, Amit I. Single‑cell genomics: a stepping stone for future immu‑nology discoveries. Cell. 2018;172(1–2):14–21.
18. Neu KE, Tang Q, Wilson PC, Khan AA. Single‑cell genomics: approaches and utility in immunology. Trends Immunol. 2017;38(2):140–9.
19. Chen HI, Jin Y, Huang Y, Chen Y. Detection of high variability in gene expression from single‑cell RNA‑seq profiling. BMC Genom. 2016;17(Suppl 7):508.
20. Clatworthy MR. How to find a resident kidney macrophage: the single‑cell sequencing solution. J Am Soc Nephrol. 2019;30(5):715–6.
21. Malone AF, Humphreys BD. Single‑cell transcriptomics and solid organ transplantation. Transplantation. 2019;103(9):1776–82.
22. Macparland SA, Liu JC, Ma XZ, Innes BT, Bartczak AM, Gage BK, et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun. 2018;9(1):4383.
23. Wang W, Penland L, Gokce O, Croote D, Quake SR. High fidelity hypo‑thermic preservation of primary tissues in organ transplant preservative for single cell transcriptome analysis. BMC Genom. 2018;19(1):140.
24. Guillaumet‑Adkins A, Rodriguez‑Esteban G, Mereu E, Mendez‑Lago M, Jaitin DA, Villanueva A, et al. Single‑cell transcriptome conservation in cryopreserved cells and tissues. Genome Biol. 2017;18(1):45.
25. Codeluppi S, Borm LE, Zeisel A, La Manno G, Van Lunteren JA, Svensson CI, et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat Methods. 2018;15(11):932–5.
26. Ståhl PL, Salmén F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353:78–82.
27. Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet‑Adkins A, Smets M, et al. Comparative analysis of single‑cell RNA sequencing methods. Mol Cell. 2017;65(4):631‑43.e4.
28. Lafzi A, Moutinho C, Picelli S, Heyn H. Tutorial: guidelines for the experimental design of single‑cell RNA sequencing studies. Nat Protoc. 2018;13(12):2742–57.
29. Munoz‑Manchado AB, Bengtsson Gonzales C, Zeisel A, Munguba H, Bekkouche B, Skene NG, et al. Diversity of interneurons in the dorsal striatum revealed by single‑cell RNA sequencing and PatchSeq. Cell Rep. 2018;24(8):2179‑90.e7.
30. Attaf N, Cervera‑Marzal I, Dong C, Gil L, Renand A, Spinelli L, et al. FB5P‑seq: FACS‑based 5‑prime end single‑cell RNA‑seq for integrative analysis of transcriptome and antigen receptor repertoire in B and T cells. Front Immunol. 2020;11:216.
31. Picelli S, Faridani OR, Björklund ÅK, Winberg G, Sagasser S, Sandberg R. Full‑length RNA‑seq from single cells using Smart‑seq2. Nat Protoc. 2014;9(1):171–81.
32. Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart‑seq2 for sensitive full‑length transcriptome profiling in single cells. Nat Methods. 2013;10(11):1096–8.
33. Sasagawa Y, Nikaido I, Hayashi T, Danno H, Uno KD, Imai T, et al. Quartz‑Seq: a highly reproducible and sensitive single‑cell RNA sequencing method, reveals non‑genetic gene‑expression heterogeneity. Genome Biol. 2013;14(4):R31.
34. Sasagawa Y, Danno H, Takada H, Ebisawa M, Tanaka K, Hayashi T, et al. Quartz‑Seq2: a high‑throughput single‑cell RNA‑sequencing method that effectively uses limited sequence reads. Genome Biol. 2018;19(1):29.
35. Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, et al. Drop‑let barcoding for single‑cell transcriptomics applied to embryonic stem cells. Cell. 2015;161(5):1187–201.
36. Hashimshony T, Senderovich N, Avital G, Klochendler A, De Leeuw Y, Anavy L, et al. CEL‑Seq2: sensitive highly‑multiplexed single‑cell RNA‑Seq. Genome Biol. 2016;17:77.
37. Hashimshony T, Wagner F, Sher N, Yanai I. CEL‑Seq: Single‑cell RNA‑Seq by multiplexed linear amplification. Cell Rep. 2012;2(3):666–73.
38. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome‑wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202–14.
39. Chen G, Ning B, Shi T. Single‑cell RNA‑seq technologies and related computational data analysis. Front Genet. 2019;10:317.
40. Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA. The technology and biology of single‑cell RNA sequencing. Mol Cell. 2015;58(4):610–20.
41. Gupta RK, Kuznicki J. Biological and medical importance of cellular heterogeneity deciphered by single‑cell RNA sequencing. Cells. 2020;9(8):E1751.
42. Zou A, Ramanathan S, Dale RC, Brilot F. Single‑cell approaches to inves‑tigate B cells and antibodies in autoimmune neurological disorders. Cell Mol Immunol. 2021;18(2):294–306.
43. Sheng K, Cao W, Niu Y, Deng Q, Zong C. Effective detection of vari‑ation in single‑cell transcriptomes using MATQ‑seq. Nat Methods. 2017;14(3):267–70.
44. Woodsworth DJ, Castellarin M, Holt RA. Sequence analysis of T‑cell repertoires in health and disease. Genome Med. 2013;5(10):98.
45. Salomon R, Kaczorowski D, Valdes‑Mora F, Nordon RE, Neild A, Farbehi N, et al. Droplet‑based single cell RNAseq tools: a practical guide. Lab Chip. 2019;19(10):1706–27.
46. Ni J, Hu C, Li H, Li X, Fu Q, Czajkowsky DM, et al. Significant improve‑ment in data quality with simplified SCRB‑seq. Acta Biochim Biophys Sin (Shanghai). 2020;52(4):457–9.
47. Islam S, Kjallquist U, Moliner A, Zajac P, Fan JB, Lonnerberg P, et al. Characterization of the single‑cell transcriptional landscape by highly multiplex RNA‑seq. Genome Res. 2011;21(7):1160–7.
48. Keren‑Shaul H, Kenigsberg E, Jaitin DA, David E, Paul F, Tanay A, et al. MARS‑seq2.0: an experimental and analytical pipeline for indexed sorting combined with single‑cell RNA sequencing. Nat Protoc. 2019;14(6):1841–62.
Page 10 of 11Wang et al. Military Med Res (2021) 8:45
49. Hicks SC, Townes FW, Teng M, Irizarry RA. Missing data and technical variability in single‑cell RNA‑sequencing experiments. Biostatistics. 2018;19(4):562–78.
50. Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, et al. Tackling the widespread and critical impact of batch effects in high‑throughput data. Nat Rev Genet. 2010;11(10):733–9.
51. Risso D, Ngai J, Speed TP, Dudoit S. Normalization of RNA‑seq data using factor analysis of control genes or samples. Nat Biotechnol. 2014;32(9):896–902.
52. Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, et al. Reference‑based analysis of lung single‑cell sequencing reveals a transitional profi‑brotic macrophage. Nat Immunol. 2019;20(2):163–72.
53. Zhang X, Lan Y, Xu J, Quan F, Zhao E, Deng C, et al. Cell Marker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 2019;47(D1):D721–8.
54. Liao J, Yu Z, Chen Y, Bao M, Zou C, Zhang H, et al. Single‑cell RNA sequencing of human kidney. Sci Data. 2020;7(1):4.
55. Kharchenko PV, Silberstein L, Scadden DT. Bayesian approach to single‑cell differential expression analysis. Nat Methods. 2014;11(7):740–2.
56. Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO, Mccarthy DJ, Marioni JC, et al. Classification of low quality cells from single‑cell RNA‑seq data. Genome Biol. 2016;17:29.
57. Liu Y, Hu J, Liu D, Zhou S, Liao J, Liao G, et al. Single‑cell analysis reveals immune landscape in kidneys of patients with chronic trans‑plant rejection. Theranostics. 2020;10(19):8851–62.
58. Prabhakaran S, Azizi E, Carr A, Pe’er D. Dirichlet process mixture model for correcting technical variation in single‑cell gene expres‑sion data. JMLR Workshop Conf Proc. 2016;48:1070–9.
59. Bacher R, Kendziorski C. Design and computational analysis of single‑cell RNA‑sequencing experiments. Genome Biol. 2016;17:63.
60. van der Maaten L, Hinton G. Visualizing data using t‑SNE. J Mach Learn Res. 2008;9:2579–605.
61. Levine JH, Simonds EF, Bendall SC, Davis KL, El Amir AD, Tadmor MD, et al. Data‑driven phenotypic dissection of AML reveals progenitor‑like cells that correlate with prognosis. Cell. 2015;162(1):184–97.
62. Becht E, Mcinnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, et al. Dimensionality reduction for visualizing single‑cell data using UMAP. Nat Biotechnol. 2018;37:38–44.
63. Xu C, Su Z. Identification of cell types from single‑cell tran‑scriptomes using a novel clustering method. Bioinformatics. 2015;31(12):1974–80.
64. Pierson E, Yau C. ZIFA: Dimensionality reduction for zero‑inflated single‑cell gene expression analysis. Genome Biol. 2015;16:241.
65. Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate integration of single‑cell data with Harmony. Nat Methods. 2019;16(12):1289–96.
66. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseu‑dotemporal ordering of single cells. Nat Biotechnol. 2014;32(4):381–6.
67. Qiu X, Hill A, Packer J, Lin D, Ma YA, Trapnell C. Single‑cell mRNA quantification and differential analysis with Census. Nat Methods. 2017;14(3):309–15.
68. Ji Z, Ji H. TSCAN: pseudo‑time reconstruction and evaluation in single‑cell RNA‑seq analysis. Nucleic Acids Res. 2016;44(13):e117.
69. Setty M, Tadmor MD, Reich‑Zeliger S, Angel O, Salame TM, Kathail P, et al. Wishbone identifies bifurcating developmental trajectories from single‑cell data. Nat Biotechnol. 2016;34(6):637–45.
70. Street K, Risso D, Fletcher RB, Das D, Ngai J, Yosef N, et al. Slingshot: cell lineage and pseudotime inference for single‑cell transcriptomics. BMC Genom. 2018;19(1):477.
71. Haghverdi L, Büttner M, Wolf FA, Buettner F, Theis FJ. Diffusion pseudotime robustly reconstructs lineage branching. Nat Methods. 2016;13(10):845–8.
72. Bendall SC, Davis KL, Amir EAD, Tadmor MD, Simonds EF, Chen TJ, et al. Single‑cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell. 2014;157(3):714–25.
73. Saelens W, Cannoodt R, Todorov H, Saeys Y. A comparison of single‑cell trajectory inference methods. Nat Biotechnol. 2019;37(5):547–54.
74. Dangi A, Yu S, Luo X. Emerging approaches and technologies in transplantation: the potential game changers. Cell Mol Immunol. 2019;16(4):334–42.
75. Noé A, Cargill TN, Nielsen CM, Russell AJC, Barnes E. The applica‑tion of single‑cell RNA sequencing in vaccinology. J Immunol Res. 2020;2020:8624963.
76. See P, Lum J, Chen J, Ginhoux F. A single‑cell sequencing guide for immunologists. Front Immunol. 2018;9:2425.
77. de Simone M, Rossetti G, Pagani M. Single cell T cell receptor sequenc‑ing: techniques and future challenges. Front Immunol. 2018;9:1638.
78. Zheng P, Tamaresis J, Thangavelu G, Xu L, You X, Blazar BR, et al. Recipient‑specific T‑cell repertoire reconstitution in the gut following murine hematopoietic cell transplant. Blood Adv. 2020;4(17):4232–43.
79. Stubbington MJT, Lönnberg T, Proserpio V, Clare S, Speak AO, Dougan G, et al. T cell fate and clonality inference from single‑cell transcriptomes. Nat Methods. 2016;13(4):329–32.
80. Morris H, Dewolf S, Robins H, Sprangers B, Locascio SA, Shonts BA, et al. Tracking donor‑reactive T cells: evidence for clonal deletion in tolerant kidney transplant patients. Sci Transl Med. 2015;7(272):272ra10.
81. DeWolf S, Sykes M. Alloimmune T cells in transplantation. J Clin Invest. 2017;127(7):2473–81.
82. Alachkar H, Mutonga M, Kato T, Kalluri S, Kakuta Y, Uemura M, et al. Quantitative characterization of T‑cell repertoire and biomarkers in kidney transplant rejection. BMC Nephrol. 2016;17(1):181.
83. Yew PY, Alachkar H, Yamaguchi R, Kiyotani K, Fang H, Yap KL, et al. Quantitative characterization of T‑cell repertoire in allogeneic hemat‑opoietic stem cell transplant recipients. Bone Marrow Transplant. 2015;50(9):1227–34.
84. Halloran PF, Pereira AB, Chang J, Matas A, Picton M, De Freitas D, et al. Potential impact of microarray diagnosis of T cell‑mediated rejec‑tion in kidney transplants: the INTERCOM study. Am J Transplant. 2013;13(9):2352–63.
85. Stewart BJ, Ferdinand JR, Clatworthy MR. Using single‑cell technologies to map the human immune system: implications for nephrology. Nat Rev Nephrol. 2020;16(2):112–28.
86. Reinders ME, Rabelink TJ, Briscoe DM. Angiogenesis and endothelial cell repair in renal disease and allograft rejection. J Am Soc Nephrol. 2006;17(4):932–42.
87. Sis B, Jhangri GS, Bunnag S, Allanach K, Kaplan B, Halloran PF. Endothe‑lial gene expression in kidney transplants with alloantibody indicates antibody‑mediated damage despite lack of C4d staining. Am J Trans‑plant. 2009;9(10):2312–23.
88. Malone AF, Wu H, Fronick C, Fulton R, Gaut JP, Humphreys BD. Harness‑ing expressed single nucleotide variation and single cell rna sequenc‑ing to define immune cell chimerism in the rejecting kidney transplant. J Am Soc Nephrol. 2020;31(9):1977–86.
89. Bellan C, Amato T, Carmellini M, Onorati M, D’amuri A, Leoncini L, et al. Analysis of the IgVH genes in T cell‑mediated and antibody‑mediated rejection of the kidney graft. J Clin Pathol. 2011;64(1):47–53.
90. Dangi A, Natesh NR, Husain I, Ji Z, Barisoni L, Kwun J, et al. Single cell transcriptomics of mouse kidney transplants reveals a myeloid cell pathway for transplant rejection. JCI Insight. 2020;5(20):e141321.
91. Varma E, Luo X, Muthukumar T. Dissecting the human kidney allograft transcriptome: single‑cell RNA sequencing. Curr Opin Organ Transplant. 2021;26(1):43–51.
92. Lei H, Reinke P, Volk HD, Lv Y, Wu R. Mechanisms of immune tolerance in liver transplantation‑crosstalk between alloreactive T cells and liver cells with therapeutic prospects. Front Immunol. 2019;10:2667.
93. Dai H, Zheng Y, Thomson AW, Rogers NM. Transplant tolerance induc‑tion: insights from the liver. Front Immunol. 2020;11:1044.
94. Feng S, Bucuvalas J. Tolerance after liver transplantation: Where are we? Liver Transpl. 2017;23(12):1601–14.
95. Thomson AW, Vionnet J, Sanchez‑Fueyo A. Understanding, predicting and achieving liver transplant tolerance: from bench to bedside. Nat Rev Gastroenterol Hepatol. 2020;17(12):719–39.
96. Choudhary NS, Saigal S, Bansal RK, Saraf N, Gautam D, Soin AS. Acute and chronic rejection after liver transplantation: what a clinician needs to know. J Clin Exp Hepatol. 2017;7(4):358–66.
97. Feng S, Bucuvalas JC, Demetris AJ, Burrell BE, Spain KM, Kanaparthi S, et al. Evidence of chronic allograft injury in liver biopsies from
Page 11 of 11Wang et al. Military Med Res (2021) 8:45
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long‑term pediatric recipients of liver transplants. Gastroenterology. 2018;155(6):1838‑51.e7.
98. Misharin AV, Morales‑Nebreda L, Reyfman PA, Cuda CM, Walter JM, Mcquattie‑Pimentel AC, et al. Monocyte‑derived alveolar macrophages drive lung fibrosis and persist in the lung over the life span. J Exp Med. 2017;214(8):2387–404.
99. Mccubbrey AL, Barthel L, Mohning MP, Redente EF, Mould KJ, Thomas SM, et al. Deletion of c‑FLIP from CD11bhi macrophages prevents development of bleomycin‑induced lung fibrosis. Am J Respir Cell Mol Biol. 2018;58(1):66–78.
100. Reyfman PA, Walter JM, Joshi N, Anekalla KR, Mcquattie‑Pimentel AC, Chiu S, et al. Single‑cell transcriptomic analysis of human lung provides insights into the pathobiology of pulmonary fibrosis. Am J Respir Crit Care Med. 2018;199(12):1517–36.
101. Mould KJ, Moore CM, Mcmanus SA, Mccubbrey AL, Mcclendon JD, Griesmer CL, et al. Airspace macrophages and monocytes exist in transcriptionally distinct subsets in healthy adults. Am J Respir Crit Care Med. 2021;203(8):946–56.
102. Snyder ME, Finlayson MO, Connors TJ, Dogra P, Senda T, Bush E, et al. Generation and persistence of human tissue‑resident memory T cells in lung transplantation. Sci Immunol. 2019;4(33):eaav5581.
103. Dong F, Hao S, Zhang S, Zhu C, Cheng H, Yang Z, et al. Differentiation of transplanted haematopoietic stem cells tracked by single‑cell transcrip‑tomic analysis. Nat Cell Biol. 2020;22(6):630–9.
104. Fujisaki J, Wu J, Carlson AL, Silberstein L, Putheti P, Larocca R, et al. In vivo imaging of Treg cells providing immune privilege to the haema‑topoietic stem‑cell niche. Nature. 2011;474(7350):216–9.
105. Beilhack A, Schulz S, Baker J, Beilhack GF, Wieland CB, Herman EI, et al. In vivo analyses of early events in acute graft‑versus‑host disease reveal sequential infiltration of T‑cell subsets. Blood. 2005;106(3):1113–22.
106. DiRienzo CG, Murphy GF, Jones SC, Korngold R, Friedman TM. T‑cell receptor Valpha spectratype analysis of a CD4‑mediated T‑cell response against minor histocompatibility antigens involved in severe graft‑versus‑host disease. Biol Blood Marrow Transplant. 2006;12(8):818–27.
107. Divito SJ, Aasebo AT, Matos TR, Hsieh PC, Collin M, Elco CP, et al. Periph‑eral host T cells survive hematopoietic stem cell transplantation and promote graft‑versus‑host disease. J Clin Invest. 2020;130(9):4624–36.
108. Habib N, Avraham‑Davidi I, Basu A, Burks T, Shekhar K, Hofree M, et al. Massively parallel single‑nucleus RNA‑seq with DroNc‑seq. Nat Meth‑ods. 2017;14(10):955–8.
109. Babel N, Stervbo U, Reinke P, Volk HD. The identity card of T cells‑clinical utility of T‑cell receptor repertoire analysis in transplantation. Transplan‑tation. 2019;103(8):1544–55.
110. Chen H, Ye F, Guo G. Revolutionizing immunology with single‑cell RNA sequencing. Cell Mol Immunol. 2019;16(3):242–9.
111. Denisenko E, Guo BB, Jones M, Hou R, De Kock L, Lassmann T, et al. Systematic assessment of tissue dissociation and storage biases in single‑cell and single‑nucleus RNA‑seq workflows. Genome Biol. 2020;21(1):130.
112. Wu H, Kirita Y, Donnelly EL, Humphreys BD. Advantages of single‑nucleus over single‑cell RNA sequencing of adult kidney: rare cell
types and novel cell states revealed in fibrosis. J Am Soc Nephrol. 2019;30(1):23–32.
113. Slyper M, Porter CBM, Ashenberg O, Waldman J, Drokhlyansky E, Wakiro I, et al. A single‑cell and single‑nucleus RNA‑Seq toolbox for fresh and frozen human tumors. Nat Med. 2020;26(5):792–802.
114. Roy AL, Conroy RS. Toward mapping the human body at a cellular resolution. Mol Biol Cell. 2018;29(15):1779–85.
115. Vegh P, Haniffa M. The impact of single‑cell RNA sequencing on under‑standing the functional organization of the immune system. Brief Funct Genom. 2018;17(4):265–72.
116. Zeng T, Dai H. Single‑cell rna sequencing‑based computational analysis to describe disease heterogeneity. Front Genet. 2019;10:629.
117. Schlitzer A, Sivakamasundari V, Chen J, Sumatoh HR, Schreuder J, Lum J, et al. Identification of cDC1‑ and cDC2‑committed DC progenitors reveals early lineage priming at the common DC progenitor stage in the bone marrow. Nat Immunol. 2015;16(7):718–28.
118. Pang WW, Price EA, Sahoo D, Beerman I, Maloney WJ, Rossi DJ, et al. Human bone marrow hematopoietic stem cells are increased in frequency and myeloid‑biased with age. Proc Natl Acad Sci USA. 2011;108(50):20012–7.
119. Jaitin DA, Kenigsberg E, Keren‑Shaul H, Elefant N, Paul F, Zaretsky I, et al. Massively parallel single‑cell RNA‑seq for marker‑free decomposition of tissues into cell types. Science. 2014;343(6172):776–9.
120. Miyamoto DT, Zheng Y, Wittner BS, Lee RJ, Zhu H, Broderick KT, et al. RNA‑Seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science. 2015;349(6254):1351–6.
121. Keren‑Shaul H, Spinrad A, Weiner A, Matcovitch‑Natan O, Dvir‑Sztern‑feld R, Ulland TK, et al. A unique microglia type associated with restrict‑ing development of Alzheimer’s disease. Cell. 2017;169(7):1276‑90.e17.
122. Myklebust JH, Brody J, Kohrt HE, Kolstad A, Czerwinski DK, Walchli S, et al. Distinct patterns of B‑cell receptor signaling in non‑Hodgkin lym‑phomas identified by single‑cell profiling. Blood. 2017;129(6):759–70.
123. Young RM, Staudt LM. Targeting pathological B cell receptor signalling in lymphoid malignancies. Nat Rev Drug Discov. 2013;12(3):229–43.
124. Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:14049.
125. Bjorklund AK, Forkel M, Picelli S, Konya V, Theorell J, Friberg D, et al. The heterogeneity of human CD127(+) innate lymphoid cells revealed by single‑cell RNA sequencing. Nat Immunol. 2016;17(4):451–60.
126. Paul F, Arkin Y, Giladi A, Jaitin DA, Kenigsberg E, Keren‑Shaul H, et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell. 2016;164(1–2):325.
127. Ye Y, Song H, Zhang J, Shi S. Understanding the biology and patho‑genesis of the kidney by single‑cell transcriptomic analysis. Kidney Dis (Basel). 2018;4(4):214–25.
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